Service Message Classification Using Unsupervised Topic-Theme Analysis
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Solution Overview
Problem
Managing and optimizing the large volume and complexity of data objects generated by application frameworks, particularly service tickets, is challenging due to inefficient resource usage and the difficulty in obtaining meaningful insights without extensive manual or statistical analysis, which is slow, costly, and prone to human error.
Innovation Solution
Utilizing unsupervised and supervised machine learning models, including natural language processing and large language models, to extract topics, themes, and classifications from service message data objects, generating dashboard visualizations for comprehensive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or statistical analysis is used to obtain insights from service message data objects, then meaningful insights can be obtained, but the process is slow, costly, and prone to human error
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated machine learning models. Specifically, unsupervised learning models automatically extract topics and themes from service message data objects, while supervised learning models classify these themes, eliminating the need for slow manual statistical analysis and reducing both time loss and human error.
Solution Approach 2:
The patent introduces machine learning models as intermediary systems between raw service message data and actionable insights. These models act as mediators that automatically process, analyze, and transform large volumes of service message data into classified themes and topics, bridging the gap between raw data and meaningful insights without requiring manual intervention.
2Loss of information
If extensive manual analysis is performed on service message data objects, then comprehensive insights can be obtained, but resource costs increase
Solution Approach 1:
The patent segments the analysis process into distinct stages handled by different machine learning models. Unsupervised learning models first extract topics from service messages, then extract themes from those topics, and finally supervised learning models classify the themes. This segmentation allows comprehensive insight extraction while distributing computational load efficiently across specialized models.
Solution Approach 2:
The patent changes the parameters of data representation through multiple transformation stages. Service messages are transformed into topic representations, which are then transformed into theme representations, with each transformation optimizing the data for the next processing stage. This parameter transformation enables comprehensive analysis while reducing computational complexity at each stage.
3Productivity
If traditional methods are used to manage service message data objects, then system stability is maintained, but productivity and efficiency decrease
Solution Approach 1:
The patent implements self-service through automated machine learning pipelines that independently process service message data objects. The unsupervised and supervised learning models automatically extract, classify, and organize data without requiring manual configuration or intervention, enabling high productivity while the modular architecture manages system complexity through clear separation of concerns.
Data Source
AI summary
Methods, apparatuses, or computer program products that process service message data objects via unsupervised machine learning to provide service message classifications. In some examples, a first feature set is extracted from a plurality of service message data objects associated with an application framework, an unsupervised natural language processing model is applied to the first feature set to generate a plurality of topic data objects, a second feature set is extracted from the plurality of topic data objects, a large language model is applied to the second feature set to generate a plurality of theme data objects representative of respective hierarchical theme classifications for the plurality of topic data objects, and a rendering of a dashboard visualization is initiated via an electronic interface based at least in part on the plurality of theme data objects.